Summary of Language-image Models with 3d Understanding, by Jang Hyun Cho et al.
Language-Image Models with 3D Understanding
by Jang Hyun Cho, Boris Ivanovic, Yulong Cao, Edward Schmerling, Yue Wang, Xinshuo Weng, Boyi Li, Yurong You, Philipp Krähenbühl, Yan Wang, Marco Pavone
First submitted to arxiv on: 6 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel large language model, Cube-LLM, that extends the capabilities of multi-modal large language models (MLLMs) to ground and reason about images in 3-dimensional space. The authors develop a large-scale pre-training dataset called LV3D by combining existing 2D and 3D recognition datasets under a common task formulation. They then pre-train Cube-LLM on this dataset, showing that pure data scaling enables strong 3D perception capabilities without 3D-specific architectural design or training objective. The model exhibits intriguing properties similar to LLMs, including chain-of-thought prompting for 3D understanding from 2D context information, following complex and diverse instructions, and adapting to versatile input and output formats. Experimental results on outdoor benchmarks demonstrate that Cube-LLM significantly outperforms existing baselines by a large margin. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how a new type of artificial intelligence model called Cube-LLM can understand and work with 3D images. This is important because many real-world situations, like self-driving cars or robots, need to interact with the world in 3D. The researchers created a special training dataset that combines existing datasets for recognizing objects in both 2D and 3D. They then trained their new model on this dataset, showing it can learn to understand 3D images without needing special 3D-focused design or training. The model can even take instructions and adjust its behavior based on the input. It’s impressive because it outperforms other models in certain tasks. |
Keywords
» Artificial intelligence » Large language model » Multi modal » Prompting
